Heart-muscle-tissue-viability assessment is essential for surgery and therapy planning following a heart attack. In particular, the proportion of viable myocardium is a major factor in determining whether a patient may benefit from a revascularization procedure. In addition to estimating the left ventricle thickness and thickening, it is possible to visualize normal, ischemic and non-viable areas with high spatial resolution, using contrast-enhanced imaging techniques and particularly late-enhancement magnetic resonance (LEMR). To locate and quantify non-viable tissues, the first step consists in delineating the endo- and epicardial contours (in other words, segmenting the myocardium) in every slice (typically in 10 to 12 slices) of the LEMR short-axis volume data, which is tedious and time-consuming when carried out manually. However, automatic myocardium segmentation is challenging and seldom implemented in current commercial products. Designing an automatic method to delineate the endo- and epicardial contours is difficult, mainly because of the non-homogeneity of the myocardium tissue resulting from contrast agent accumulation in ischemic and non-viable areas.
FIG. 1 shows an image computed from a slice of LEMR SA image data. The first picture 11 shows the image without myocardial contours. The second picture 12 shows the image with myocardial contours manually drawn by experts. The border between the blood pool 110 and the abnormal tissues 120 inside the myocardium is difficult to locate. The scar region 120 appears in white, while the healthy part 130 is dark and the surrounding organs vary from gray to dark. Moreover, the borders of the white regions often appear very fuzzy, especially if they are close to the blood pool, which makes correctly locating the endocardium particularly difficult. Thus, the challenge is to extract a structure like the myocardium, which may contain both dark and white areas, from a textured environment.
An automatic segmentation algorithm for segmenting LEMR SA images is described in an article by E. Dikici, T. O'Donnell, R. Setser and R. D. White, “Quantification of delayed enhancement MR images”, Proc. of the 7th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'04), LNCS Series, Vol. 3216, Springer, pp. 250-257, 2004.